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CORRECTING FOR MEASUREMENT ERROR IN AN EXPOSURE‐RESPONSE RELATIONSHIP BASED ON DICHOTOMISING A CONTINUOUS DEPENDENT VARIABLE
Author(s) -
Irwig Les M.,
Groeneveld Hennie T.,
Simpson Judy M.
Publication year - 1990
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1990.tb01022.x
Subject(s) - statistics , continuous variable , outcome (game theory) , observational error , mathematics , variable (mathematics) , linear regression , reliability (semiconductor) , random error , regression analysis , physics , mathematical analysis , power (physics) , mathematical economics , quantum mechanics
Summary Random error in a continuous outcome variable does not affect its regression on a predictor. However, when a continuous outcome variable is dichotomised, random measurement error results in a flatter exposure‐response relationship with a higher intercept. Although this consequence is similar to the effect of misclassification in a binary outcome variable, it cannot be corrected using techniques appropriate for binary data. Conditional distributions of the measurements of the continuous outcome variable can be corrected if the reliability coefficient of the measurements can be estimated. An unbiased estimate of the exposure‐response relationship is then easily calculated. This procedure is demonstrated using data on the relationship between smoking and the development of airway obstruction.